Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations52413
Missing cells69642
Missing cells (%)8.3%
Duplicate rows1104
Duplicate rows (%)2.1%
Total size in memory8.8 MiB
Average record size in memory176.3 B

Variable types

Numeric14
Categorical2

Alerts

14615_FERM0101.DO_2_PV has constant value "0.0"Constant
Dataset has 1104 (2.1%) duplicate rowsDuplicates
14615_FERM0101.PUMP_1_PV is highly imbalanced (99.9%)Imbalance
14615_FERM0101.Temperatura_PV has 4353 (8.3%) missing valuesMissing
14615_FERM0101.Single_Use_pH_PV has 4353 (8.3%) missing valuesMissing
14615_FERM0101.Single_Use_DO_PV has 4353 (8.3%) missing valuesMissing
14615_FERM0101.PUMP_2_TOTAL has 4352 (8.3%) missing valuesMissing
14615_FERM0101.PUMP_2_PV has 4353 (8.3%) missing valuesMissing
14615_FERM0101.PUMP_1_TOTAL has 4352 (8.3%) missing valuesMissing
14615_FERM0101.PUMP_1_PV has 4353 (8.3%) missing valuesMissing
14615_FERM0101.pH_2_PV has 4352 (8.3%) missing valuesMissing
14615_FERM0101.pH_1_PV has 4352 (8.3%) missing valuesMissing
14615_FERM0101.Load_Cell_Net_PV has 4352 (8.3%) missing valuesMissing
14615_FERM0101.Gas_Overlay_PV has 4353 (8.3%) missing valuesMissing
14615_FERM0101.DO_2_PV has 4353 (8.3%) missing valuesMissing
14615_FERM0101.DO_1_PV has 4353 (8.3%) missing valuesMissing
14615_FERM0101.Biocontainer_Pressure_PV has 4352 (8.3%) missing valuesMissing
14615_FERM0101.Air_Sparge_PV has 4353 (8.3%) missing valuesMissing
14615_FERM0101.Agitation_PV has 4353 (8.3%) missing valuesMissing
14615_FERM0101.PUMP_2_TOTAL has 13306 (25.4%) zerosZeros
14615_FERM0101.PUMP_2_PV has 41516 (79.2%) zerosZeros
14615_FERM0101.PUMP_1_TOTAL has 3816 (7.3%) zerosZeros
14615_FERM0101.Load_Cell_Net_PV has 1541 (2.9%) zerosZeros
14615_FERM0101.Gas_Overlay_PV has 16476 (31.4%) zerosZeros
14615_FERM0101.DO_1_PV has 36033 (68.7%) zerosZeros
14615_FERM0101.Air_Sparge_PV has 44083 (84.1%) zerosZeros
14615_FERM0101.Agitation_PV has 25124 (47.9%) zerosZeros

Reproduction

Analysis started2024-09-29 18:19:29.483670
Analysis finished2024-09-29 18:19:44.500343
Duration15.02 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

14615_FERM0101.Temperatura_PV
Real number (ℝ)

MISSING 

Distinct24033
Distinct (%)50.0%
Missing4353
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean19.022394
Minimum2.8799988
Maximum31.22899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:19:44.546922image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2.8799988
5-th percentile4.6246869
Q114.935999
median17.176927
Q328.551722
95-th percentile29.631995
Maximum31.22899
Range28.348991
Interquartile range (IQR)13.615724

Descriptive statistics

Standard deviation7.6664354
Coefficient of variation (CV)0.40302159
Kurtosis-0.80942924
Mean19.022394
Median Absolute Deviation (MAD)5.2489456
Skewness-0.020907413
Sum914216.25
Variance58.774232
MonotonicityNot monotonic
2024-09-29T20:19:44.619081image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.60799561 1552
 
3.0%
29.59200439 1548
 
3.0%
29.63199463 1057
 
2.0%
29.56800537 1038
 
2.0%
29.55999756 517
 
1.0%
29.64000244 507
 
1.0%
3.191998291 362
 
0.7%
3.208001709 327
 
0.6%
17.67199707 249
 
0.5%
29.55200195 241
 
0.5%
Other values (24023) 40662
77.6%
(Missing) 4353
 
8.3%
ValueCountFrequency (%)
2.879998779 1
 
< 0.1%
3.032000732 1
 
< 0.1%
3.088000488 1
 
< 0.1%
3.096002197 1
 
< 0.1%
3.111999512 2
 
< 0.1%
3.12800293 5
< 0.1%
3.128008392 1
 
< 0.1%
3.128163024 1
 
< 0.1%
3.12816351 1
 
< 0.1%
3.128260176 1
 
< 0.1%
ValueCountFrequency (%)
31.22898975 1
 
< 0.1%
31.2270481 1
 
< 0.1%
31.21600342 3
< 0.1%
31.2087176 1
 
< 0.1%
31.2 3
< 0.1%
31.19630081 1
 
< 0.1%
31.19200439 2
< 0.1%
31.16800537 2
< 0.1%
31.16043226 1
 
< 0.1%
31.13274982 1
 
< 0.1%

14615_FERM0101.Single_Use_pH_PV
Real number (ℝ)

MISSING 

Distinct2486
Distinct (%)5.2%
Missing4353
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean596.47506
Minimum-794.33599
Maximum805.696
Zeros0
Zeros (%)0.0%
Negative40
Negative (%)0.1%
Memory size2.8 MiB
2024-09-29T20:19:44.687715image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-794.33599
5-th percentile5.7200001
Q15.984
median799.88799
Q3799.93599
95-th percentile800.06401
Maximum805.696
Range1600.032
Interquartile range (IQR)793.95199

Descriptive statistics

Standard deviation346.86955
Coefficient of variation (CV)0.58153237
Kurtosis-0.74471465
Mean596.47506
Median Absolute Deviation (MAD)0.095996094
Skewness-1.1183187
Sum28666591
Variance120318.49
MonotonicityNot monotonic
2024-09-29T20:19:44.763127image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
799.9199707 4669
 
8.9%
799.7919922 3854
 
7.4%
799.8959961 3390
 
6.5%
799.9040039 2359
 
4.5%
799.9439941 1929
 
3.7%
799.8640137 1877
 
3.6%
799.952002 1611
 
3.1%
799.8799805 1506
 
2.9%
799.9120117 1322
 
2.5%
800.0560059 1317
 
2.5%
Other values (2476) 24226
46.2%
(Missing) 4353
 
8.3%
ValueCountFrequency (%)
-794.3359863 1
 
< 0.1%
-788.3519531 1
 
< 0.1%
-0.2719997406 1
 
< 0.1%
-0.263999939 1
 
< 0.1%
-0.2240001678 1
 
< 0.1%
-0.2 5
< 0.1%
-0.1759998322 1
 
< 0.1%
-0.1680000305 1
 
< 0.1%
-0.1279998779 2
 
< 0.1%
-0.1119998932 3
< 0.1%
ValueCountFrequency (%)
805.6959961 455
 
0.9%
802.4080078 537
1.0%
801.752002 109
 
0.2%
800.1199707 786
1.5%
800.0640137 1216
2.3%
800.0560059 1317
2.5%
800.0239746 1007
1.9%
800.0080078 1126
2.1%
799.9919922 456
 
0.9%
799.9759766 461
 
0.9%

14615_FERM0101.Single_Use_DO_PV
Real number (ℝ)

MISSING 

Distinct10905
Distinct (%)22.7%
Missing4353
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean577.74084
Minimum0
Maximum815.40313
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:19:44.840466image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.717587
Q1640.96787
median721.62363
Q3799.99199
95-th percentile799.99199
Maximum815.40313
Range815.40313
Interquartile range (IQR)159.02412

Descriptive statistics

Standard deviation314.92143
Coefficient of variation (CV)0.54509116
Kurtosis-0.62833474
Mean577.74084
Median Absolute Deviation (MAD)78.368359
Skewness-1.1154595
Sum27766225
Variance99175.504
MonotonicityNot monotonic
2024-09-29T20:19:44.915695image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
799.9919922 20256
38.6%
659.7328125 3794
 
7.2%
753.855957 2179
 
4.2%
693.7148926 1279
 
2.4%
640.9678711 1248
 
2.4%
716.7102539 1235
 
2.4%
695.1012207 888
 
1.7%
667.8287109 753
 
1.4%
768.0414551 582
 
1.1%
82.97983398 412
 
0.8%
Other values (10895) 15434
29.4%
(Missing) 4353
 
8.3%
ValueCountFrequency (%)
0 3
< 0.1%
1.211847604 1
 
< 0.1%
1.233964179 1
 
< 0.1%
1.244670814 1
 
< 0.1%
1.295096852 1
 
< 0.1%
1.300275692 1
 
< 0.1%
1.313364029 1
 
< 0.1%
1.361724446 1
 
< 0.1%
1.431655166 1
 
< 0.1%
1.433451477 1
 
< 0.1%
ValueCountFrequency (%)
815.403125 189
 
0.4%
812.6696289 138
 
0.3%
799.9919922 20256
38.6%
799.8291548 1
 
< 0.1%
799.391338 1
 
< 0.1%
799.278426 1
 
< 0.1%
799.077949 1
 
< 0.1%
787.4319824 1
 
< 0.1%
777.5439941 1
 
< 0.1%
775.8 1
 
< 0.1%

14615_FERM0101.PUMP_2_TOTAL
Real number (ℝ)

MISSING  ZEROS 

Distinct8950
Distinct (%)18.6%
Missing4352
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean3647.9799
Minimum0
Maximum12268.625
Zeros13306
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:19:44.991358image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2081.443
Q36996.8898
95-th percentile9120.9797
Maximum12268.625
Range12268.625
Interquartile range (IQR)6996.8898

Descriptive statistics

Standard deviation3586.8652
Coefficient of variation (CV)0.98324696
Kurtosis-1.6295878
Mean3647.9799
Median Absolute Deviation (MAD)2081.443
Skewness0.26817547
Sum1.7532556 × 108
Variance12865602
MonotonicityNot monotonic
2024-09-29T20:19:45.066907image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13306
25.4%
9120.979687 3790
 
7.2%
6505.707813 2181
 
4.2%
5668.085156 1629
 
3.1%
248.8854736 1337
 
2.6%
7370.183594 1328
 
2.5%
6996.889844 1285
 
2.5%
167.4596191 1049
 
2.0%
7987.819531 1033
 
2.0%
7659.826563 785
 
1.5%
Other values (8940) 20338
38.8%
(Missing) 4352
 
8.3%
ValueCountFrequency (%)
0 13306
25.4%
0.1468597095 1
 
< 0.1%
1.455256405 1
 
< 0.1%
2.400578172 1
 
< 0.1%
2.80128212 2
 
< 0.1%
4.28546524 1
 
< 0.1%
5.441709137 3
 
< 0.1%
7.129507257 1
 
< 0.1%
7.677305295 1
 
< 0.1%
9.401261139 1
 
< 0.1%
ValueCountFrequency (%)
12268.625 41
0.1%
12235.15078 1
 
< 0.1%
12200.79766 1
 
< 0.1%
12138.02578 1
 
< 0.1%
12083.57969 1
 
< 0.1%
12013.67734 32
0.1%
12013.36273 1
 
< 0.1%
12002.17031 2
 
< 0.1%
11992.73203 1
 
< 0.1%
11976.50078 1
 
< 0.1%

14615_FERM0101.PUMP_2_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct5904
Distinct (%)12.3%
Missing4353
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean0.5182882
Minimum0
Maximum48
Zeros41516
Zeros (%)79.2%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:19:45.143569image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.9968242
Maximum48
Range48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6944726
Coefficient of variation (CV)3.2693636
Kurtosis35.767311
Mean0.5182882
Median Absolute Deviation (MAD)0
Skewness4.2042309
Sum24908.931
Variance2.8712372
MonotonicityNot monotonic
2024-09-29T20:19:45.214715image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41516
79.2%
8 585
 
1.1%
7.610464478 13
 
< 0.1%
7.200439453 11
 
< 0.1%
2.400146484 8
 
< 0.1%
4.800292969 5
 
< 0.1%
0.2255298615 3
 
< 0.1%
0.003704071045 3
 
< 0.1%
48 2
 
< 0.1%
0.4395198822 2
 
< 0.1%
Other values (5894) 5912
 
11.3%
(Missing) 4353
 
8.3%
ValueCountFrequency (%)
0 41516
79.2%
5.020227855 × 10-61
 
< 0.1%
5.724162096 × 10-61
 
< 0.1%
1.594937473 × 10-51
 
< 0.1%
2.942391561 × 10-51
 
< 0.1%
4.492293769 × 10-51
 
< 0.1%
5.350303202 × 10-51
 
< 0.1%
0.0001122938038 1
 
< 0.1%
0.0002339555154 1
 
< 0.1%
0.0003401865835 1
 
< 0.1%
ValueCountFrequency (%)
48 2
 
< 0.1%
8 585
1.1%
7.999999282 1
 
< 0.1%
7.999982142 1
 
< 0.1%
7.999976459 1
 
< 0.1%
7.999970573 1
 
< 0.1%
7.999955503 1
 
< 0.1%
7.999946716 1
 
< 0.1%
7.999940426 1
 
< 0.1%
7.999930213 1
 
< 0.1%

14615_FERM0101.PUMP_1_TOTAL
Real number (ℝ)

MISSING  ZEROS 

Distinct158
Distinct (%)0.3%
Missing4352
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean24.139296
Minimum0
Maximum157.07986
Zeros3816
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:19:45.284363image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.2399994
median21.119997
Q331.680002
95-th percentile60.720026
Maximum157.07986
Range157.07986
Interquartile range (IQR)22.440002

Descriptive statistics

Standard deviation20.485735
Coefficient of variation (CV)0.84864678
Kurtosis3.0490869
Mean24.139296
Median Absolute Deviation (MAD)11.879997
Skewness1.5858555
Sum1160158.7
Variance419.66536
MonotonicityNot monotonic
2024-09-29T20:19:45.358534image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3816
 
7.3%
40.47999878 3812
 
7.3%
21.11999664 3547
 
6.8%
6.6 3498
 
6.7%
22.43999634 2441
 
4.7%
17.15999756 2083
 
4.0%
11.87999878 1999
 
3.8%
5.279999924 1912
 
3.6%
26.39999695 1864
 
3.6%
28.16000366 1375
 
2.6%
Other values (148) 21714
41.4%
(Missing) 4352
 
8.3%
ValueCountFrequency (%)
0 3816
7.3%
0.01962204832 1
 
< 0.1%
0.07128547617 1
 
< 0.1%
0.1315726093 1
 
< 0.1%
0.1423443709 1
 
< 0.1%
0.1841951646 1
 
< 0.1%
0.2217831827 1
 
< 0.1%
0.3634535565 1
 
< 0.1%
0.3980566611 1
 
< 0.1%
1.310444561 1
 
< 0.1%
ValueCountFrequency (%)
157.0798584 1
 
< 0.1%
100.7599487 211
 
0.4%
97.68005981 410
0.8%
96.79995117 745
1.4%
88.44005127 17
 
< 0.1%
81.84004517 42
 
0.1%
81.39996948 17
 
< 0.1%
80.95997314 93
 
0.2%
79.63997803 80
 
0.2%
77.8800415 19
 
< 0.1%

14615_FERM0101.PUMP_1_PV
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing4353
Missing (%)8.3%
Memory size2.8 MiB
0.0
48057 
48.0
 
2
50.0624194986624
 
1

Length

Max length16
Median length3
Mean length3.0003121
Min length3

Characters and Unicode

Total characters144195
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48057
91.7%
48.0 2
 
< 0.1%
50.0624194986624 1
 
< 0.1%
(Missing) 4353
 
8.3%

Length

2024-09-29T20:19:45.432180image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T20:19:45.484321image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48057
> 99.9%
48.0 2
 
< 0.1%
50.0624194986624 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 96118
66.7%
. 48060
33.3%
4 5
 
< 0.1%
8 3
 
< 0.1%
6 3
 
< 0.1%
2 2
 
< 0.1%
9 2
 
< 0.1%
5 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144195
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 96118
66.7%
. 48060
33.3%
4 5
 
< 0.1%
8 3
 
< 0.1%
6 3
 
< 0.1%
2 2
 
< 0.1%
9 2
 
< 0.1%
5 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144195
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 96118
66.7%
. 48060
33.3%
4 5
 
< 0.1%
8 3
 
< 0.1%
6 3
 
< 0.1%
2 2
 
< 0.1%
9 2
 
< 0.1%
5 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144195
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 96118
66.7%
. 48060
33.3%
4 5
 
< 0.1%
8 3
 
< 0.1%
6 3
 
< 0.1%
2 2
 
< 0.1%
9 2
 
< 0.1%
5 1
 
< 0.1%
1 1
 
< 0.1%

14615_FERM0101.pH_2_PV
Real number (ℝ)

MISSING 

Distinct3475
Distinct (%)7.2%
Missing4352
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean2.0131957
Minimum-0.26444588
Maximum105.31427
Zeros25
Zeros (%)< 0.1%
Negative19973
Negative (%)38.1%
Memory size2.8 MiB
2024-09-29T20:19:45.545453image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-0.26444588
5-th percentile-0.23476257
Q1-0.23476257
median1.5329995
Q35.5634209
95-th percentile5.8844936
Maximum105.31427
Range105.57872
Interquartile range (IQR)5.7981834

Descriptive statistics

Standard deviation3.0103852
Coefficient of variation (CV)1.4953267
Kurtosis296.54175
Mean2.0131957
Median Absolute Deviation (MAD)1.7677621
Skewness10.334694
Sum96756.198
Variance9.0624192
MonotonicityNot monotonic
2024-09-29T20:19:45.619727image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2347625732 18573
35.4%
1.564368343 2487
 
4.7%
1.676117706 2170
 
4.1%
-0.2644458771 1356
 
2.6%
1.592201424 1263
 
2.4%
1.401505852 1036
 
2.0%
1.637450981 856
 
1.6%
1.578890514 557
 
1.1%
1.675854492 555
 
1.1%
1.641864586 465
 
0.9%
Other values (3465) 18743
35.8%
(Missing) 4352
 
8.3%
ValueCountFrequency (%)
-0.2644458771 1356
 
2.6%
-0.2347625732 18573
35.4%
-0.2346772473 1
 
< 0.1%
-0.2344145706 1
 
< 0.1%
-0.2331716799 1
 
< 0.1%
-0.2330111366 1
 
< 0.1%
-0.2322276584 1
 
< 0.1%
-0.2315282666 1
 
< 0.1%
-0.2297079138 1
 
< 0.1%
-0.2108184814 8
 
< 0.1%
ValueCountFrequency (%)
105.31427 1
 
< 0.1%
93.08724365 1
 
< 0.1%
91.88721924 1
 
< 0.1%
91.58453369 1
 
< 0.1%
91.29263916 2
< 0.1%
90.9899292 3
< 0.1%
90.68723145 4
< 0.1%
90.39533691 4
< 0.1%
90.09262695 1
 
< 0.1%
8.700935857 1
 
< 0.1%

14615_FERM0101.pH_1_PV
Real number (ℝ)

MISSING 

Distinct4182
Distinct (%)8.7%
Missing4352
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean2.557953
Minimum0
Maximum38.096255
Zeros36
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:19:45.693383image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3002729
Q11.3002729
median1.4162539
Q34.1155235
95-th percentile5.8862934
Maximum38.096255
Range38.096255
Interquartile range (IQR)2.8152506

Descriptive statistics

Standard deviation1.8316659
Coefficient of variation (CV)0.71606705
Kurtosis2.3185348
Mean2.557953
Median Absolute Deviation (MAD)0.11598101
Skewness1.1834102
Sum122937.78
Variance3.3549998
MonotonicityNot monotonic
2024-09-29T20:19:45.769519image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.300272942 22903
43.7%
4.115523529 5057
 
9.6%
1.658554077 1217
 
2.3%
1.416253948 1196
 
2.3%
1.544090271 696
 
1.3%
1.497356987 680
 
1.3%
1.61123867 602
 
1.1%
1.49628067 459
 
0.9%
1.54283371 456
 
0.9%
1.667505264 390
 
0.7%
Other values (4172) 14405
27.5%
(Missing) 4352
 
8.3%
ValueCountFrequency (%)
0 36
0.1%
0.0009447473402 1
 
< 0.1%
0.002654317213 1
 
< 0.1%
0.003621569916 1
 
< 0.1%
0.005281124416 1
 
< 0.1%
0.007431849441 1
 
< 0.1%
0.00992509168 1
 
< 0.1%
0.01048191482 1
 
< 0.1%
0.01232262364 1
 
< 0.1%
0.01280210581 1
 
< 0.1%
ValueCountFrequency (%)
38.09625549 1
< 0.1%
7.595863342 1
< 0.1%
7.586955261 1
< 0.1%
7.583180324 1
< 0.1%
7.578674523 1
< 0.1%
7.578654555 1
< 0.1%
7.578651428 2
< 0.1%
7.570815323 1
< 0.1%
7.570722985 1
< 0.1%
7.570643616 1
< 0.1%

14615_FERM0101.Load_Cell_Net_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct1990
Distinct (%)4.1%
Missing4352
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean741.58854
Minimum-22.4
Maximum1711.4491
Zeros1541
Zeros (%)2.9%
Negative19766
Negative (%)37.7%
Memory size2.8 MiB
2024-09-29T20:19:45.841694image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-22.4
5-th percentile-18.4
Q1-16.4
median2.8
Q31592
95-th percentile1662.8
Maximum1711.4491
Range1733.8491
Interquartile range (IQR)1608.4

Descriptive statistics

Standard deviation800.29719
Coefficient of variation (CV)1.0791661
Kurtosis-1.9533211
Mean741.58854
Median Absolute Deviation (MAD)21.6
Skewness0.15313062
Sum35641487
Variance640475.6
MonotonicityNot monotonic
2024-09-29T20:19:45.914832image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18 2867
 
5.5%
-18.4 2528
 
4.8%
-14.4 2126
 
4.1%
-17.2 2017
 
3.8%
-17.6 1763
 
3.4%
-18.8 1542
 
2.9%
0 1541
 
2.9%
-0.4 1302
 
2.5%
-1.6 1247
 
2.4%
2 762
 
1.5%
Other values (1980) 30366
57.9%
(Missing) 4352
 
8.3%
ValueCountFrequency (%)
-22.4 2
 
< 0.1%
-22 3
 
< 0.1%
-21.6 4
 
< 0.1%
-21.2 10
 
< 0.1%
-20.8 16
 
< 0.1%
-20.4 23
 
< 0.1%
-20 28
 
0.1%
-19.68075124 1
 
< 0.1%
-19.6 47
 
0.1%
-19.2 611
1.2%
ValueCountFrequency (%)
1711.449056 1
 
< 0.1%
1710.587867 1
 
< 0.1%
1706.719481 1
 
< 0.1%
1706 17
< 0.1%
1705.6 25
< 0.1%
1705.591693 1
 
< 0.1%
1705.505852 1
 
< 0.1%
1705.457903 1
 
< 0.1%
1705.2 31
0.1%
1705.058271 1
 
< 0.1%

14615_FERM0101.Gas_Overlay_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct31585
Distinct (%)65.7%
Missing4353
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean2.7712883
Minimum0
Maximum16.001782
Zeros16476
Zeros (%)31.4%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:19:45.986662image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.9997998
Q34.0001441
95-th percentile4.0008662
Maximum16.001782
Range16.001782
Interquartile range (IQR)4.0001441

Descriptive statistics

Standard deviation2.1392589
Coefficient of variation (CV)0.77193658
Kurtosis-0.27687384
Mean2.7712883
Median Absolute Deviation (MAD)0.00053469789
Skewness-0.046576707
Sum133188.12
Variance4.5764284
MonotonicityNot monotonic
2024-09-29T20:19:46.061538image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16476
31.4%
3.999874464 1
 
< 0.1%
4.000053718 1
 
< 0.1%
3.999962357 1
 
< 0.1%
4.000154254 1
 
< 0.1%
4.000174089 1
 
< 0.1%
4.000222955 1
 
< 0.1%
4.000418269 1
 
< 0.1%
4.000196392 1
 
< 0.1%
3.994040144 1
 
< 0.1%
Other values (31575) 31575
60.2%
(Missing) 4353
 
8.3%
ValueCountFrequency (%)
0 16476
31.4%
3.95767525 1
 
< 0.1%
3.959049178 1
 
< 0.1%
3.962996066 1
 
< 0.1%
3.963635189 1
 
< 0.1%
3.963674017 1
 
< 0.1%
3.975593718 1
 
< 0.1%
3.977468455 1
 
< 0.1%
3.977937658 1
 
< 0.1%
3.978744818 1
 
< 0.1%
ValueCountFrequency (%)
16.00178203 1
< 0.1%
16.00161624 1
< 0.1%
16.00104979 1
< 0.1%
16.00076107 1
< 0.1%
16.00057974 1
< 0.1%
16.00036089 1
< 0.1%
16.00022746 1
< 0.1%
16.00009975 1
< 0.1%
15.99966771 1
< 0.1%
15.99964889 1
< 0.1%

14615_FERM0101.DO_2_PV
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing4353
Missing (%)8.3%
Memory size2.8 MiB
0.0
48060 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters144180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 48060
91.7%
(Missing) 4353
 
8.3%

Length

2024-09-29T20:19:46.130349image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-29T20:19:46.175553image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48060
100.0%

Most occurring characters

ValueCountFrequency (%)
0 96120
66.7%
. 48060
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 96120
66.7%
. 48060
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 96120
66.7%
. 48060
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 96120
66.7%
. 48060
33.3%

14615_FERM0101.DO_1_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct7654
Distinct (%)15.9%
Missing4353
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean5.8138307
Minimum0
Maximum109.58795
Zeros36033
Zeros (%)68.7%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:19:46.230343image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.2842045
95-th percentile25.302866
Maximum109.58795
Range109.58795
Interquartile range (IQR)3.2842045

Descriptive statistics

Standard deviation12.754246
Coefficient of variation (CV)2.1937767
Kurtosis12.224773
Mean5.8138307
Median Absolute Deviation (MAD)0
Skewness3.1709245
Sum279412.7
Variance162.6708
MonotonicityNot monotonic
2024-09-29T20:19:46.301448image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36033
68.7%
12.15542221 30
 
0.1%
24.23786316 29
 
0.1%
23.30582581 24
 
< 0.1%
23.0339798 24
 
< 0.1%
24.37378693 21
 
< 0.1%
12.0136528 21
 
< 0.1%
12.30244141 20
 
< 0.1%
23.66294556 20
 
< 0.1%
22.63106689 19
 
< 0.1%
Other values (7644) 11819
 
22.5%
(Missing) 4353
 
8.3%
ValueCountFrequency (%)
0 36033
68.7%
1.149908161 1
 
< 0.1%
1.296928406 1
 
< 0.1%
1.43869791 1
 
< 0.1%
1.577126503 1
 
< 0.1%
1.869573784 1
 
< 0.1%
2.168548203 1
 
< 0.1%
2.665699005 1
 
< 0.1%
2.936757851 1
 
< 0.1%
3.198258209 1
 
< 0.1%
ValueCountFrequency (%)
109.5879517 1
< 0.1%
92.80671387 1
< 0.1%
88.48379517 1
< 0.1%
88.13513545 1
< 0.1%
87.91049194 1
< 0.1%
86.59971924 1
< 0.1%
84.47548828 1
< 0.1%
84.0079895 1
< 0.1%
83.82406616 1
< 0.1%
83.66962891 1
< 0.1%

14615_FERM0101.Biocontainer_Pressure_PV
Real number (ℝ)

MISSING 

Distinct22265
Distinct (%)46.3%
Missing4352
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean148.23927
Minimum-14.840857
Maximum480
Zeros0
Zeros (%)0.0%
Negative19534
Negative (%)37.3%
Memory size2.8 MiB
2024-09-29T20:19:46.373613image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-14.840857
5-th percentile-1.6348389
Q1-0.42372254
median0.37129787
Q3480
95-th percentile480
Maximum480
Range494.84086
Interquartile range (IQR)480.42372

Descriptive statistics

Standard deviation221.86287
Coefficient of variation (CV)1.4966538
Kurtosis-1.3166685
Mean148.23927
Median Absolute Deviation (MAD)1.1738437
Skewness0.82653836
Sum7124527.7
Variance49223.133
MonotonicityNot monotonic
2024-09-29T20:19:46.446940image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
480 14851
28.3%
-0.2372680664 443
 
0.8%
-0.2170166016 349
 
0.7%
-0.2575256348 316
 
0.6%
-0.01446533203 304
 
0.6%
0.005786132813 291
 
0.6%
0.2083312988 285
 
0.5%
-0.4600708008 283
 
0.5%
-0.4803222656 259
 
0.5%
-0.6626159668 251
 
0.5%
Other values (22255) 30429
58.1%
(Missing) 4352
 
8.3%
ValueCountFrequency (%)
-14.84085693 1
< 0.1%
-10.546875 1
< 0.1%
-10.46581812 1
< 0.1%
-10.46541518 1
< 0.1%
-10.40535583 1
< 0.1%
-10.38617345 1
< 0.1%
-10.38502773 1
< 0.1%
-10.22196953 1
< 0.1%
-10.20229146 1
< 0.1%
-10.18298932 1
< 0.1%
ValueCountFrequency (%)
480 14851
28.3%
170.8269393 1
 
< 0.1%
19.67327912 1
 
< 0.1%
19.025627 1
 
< 0.1%
18.43674328 1
 
< 0.1%
17.7083313 1
 
< 0.1%
17.44502563 1
 
< 0.1%
17.08036093 1
 
< 0.1%
16.56163216 1
 
< 0.1%
16.00449437 1
 
< 0.1%

14615_FERM0101.Air_Sparge_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct3977
Distinct (%)8.3%
Missing4353
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean3.2027467
Minimum0
Maximum160.27601
Zeros44083
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:19:46.516819image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile29.3048
Maximum160.27601
Range160.27601
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.943404
Coefficient of variation (CV)4.041345
Kurtosis19.628648
Mean3.2027467
Median Absolute Deviation (MAD)0
Skewness4.3456334
Sum153924
Variance167.53171
MonotonicityNot monotonic
2024-09-29T20:19:46.589987image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44083
84.1%
62.88121338 2
 
< 0.1%
64.01446627 1
 
< 0.1%
64.34274699 1
 
< 0.1%
64.0326881 1
 
< 0.1%
63.99061313 1
 
< 0.1%
63.99311586 1
 
< 0.1%
63.9980221 1
 
< 0.1%
63.96494077 1
 
< 0.1%
64.00094422 1
 
< 0.1%
Other values (3967) 3967
 
7.6%
(Missing) 4353
 
8.3%
ValueCountFrequency (%)
0 44083
84.1%
0.002813267495 1
 
< 0.1%
0.002902076209 1
 
< 0.1%
0.007252627722 1
 
< 0.1%
0.01454566361 1
 
< 0.1%
0.02311518132 1
 
< 0.1%
0.02662493759 1
 
< 0.1%
0.03108584517 1
 
< 0.1%
0.07191738577 1
 
< 0.1%
0.07565658565 1
 
< 0.1%
ValueCountFrequency (%)
160.2760132 1
< 0.1%
160.2630737 1
< 0.1%
160.1191105 1
< 0.1%
160.1169987 1
< 0.1%
160.1064904 1
< 0.1%
160.062027 1
< 0.1%
160.0504738 1
< 0.1%
160.0120159 1
< 0.1%
159.9871997 1
< 0.1%
159.9833286 1
< 0.1%

14615_FERM0101.Agitation_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct786
Distinct (%)1.6%
Missing4353
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean29.496487
Minimum0
Maximum80
Zeros25124
Zeros (%)47.9%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:19:46.664330image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q380
95-th percentile80
Maximum80
Range80
Interquartile range (IQR)80

Descriptive statistics

Standard deviation35.796978
Coefficient of variation (CV)1.2136014
Kurtosis-1.5016824
Mean29.496487
Median Absolute Deviation (MAD)0
Skewness0.5986237
Sum1417601.2
Variance1281.4236
MonotonicityNot monotonic
2024-09-29T20:19:46.739509image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25124
47.9%
80 15079
28.8%
20 5433
 
10.4%
36 1066
 
2.0%
44 524
 
1.0%
40 29
 
0.1%
72 15
 
< 0.1%
28 5
 
< 0.1%
32 3
 
< 0.1%
48 3
 
< 0.1%
Other values (776) 779
 
1.5%
(Missing) 4353
 
8.3%
ValueCountFrequency (%)
0 25124
47.9%
16.90591273 1
 
< 0.1%
20 5433
 
10.4%
20.04541852 1
 
< 0.1%
20.0564596 1
 
< 0.1%
20.11278775 1
 
< 0.1%
20.20262012 1
 
< 0.1%
20.28988183 1
 
< 0.1%
20.31330564 1
 
< 0.1%
20.52029305 1
 
< 0.1%
ValueCountFrequency (%)
80 15079
28.8%
79.90183953 1
 
< 0.1%
79.85973765 1
 
< 0.1%
79.82890535 1
 
< 0.1%
79.75873759 1
 
< 0.1%
79.75764121 1
 
< 0.1%
79.74632652 1
 
< 0.1%
79.67000122 1
 
< 0.1%
79.65361609 1
 
< 0.1%
79.64263072 1
 
< 0.1%

Interactions

2024-09-29T20:19:42.915424image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:30.106737image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:31.112131image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:32.120066image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:33.121763image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:34.091447image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:35.041055image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:36.034041image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:36.967276image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:37.954051image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:38.941546image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:40.018473image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:40.960753image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:41.922353image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:42.983556image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:30.198308image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:31.182394image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:32.188246image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:33.188392image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:34.155659image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:35.108385image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:36.096637image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:37.034924image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:38.021708image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:19:41.025824image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:41.990275image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:43.057777image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:19:33.262157image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:19:40.293185image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:41.240385image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:42.210755image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:43.274568image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:19:31.470549image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:19:38.381375image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:39.411998image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:40.424425image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:41.375837image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:42.350372image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:43.413901image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:30.632113image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:31.612710image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:32.618008image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:33.603066image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:34.559356image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:35.537563image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:36.498108image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:37.463444image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:38.446947image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:39.482958image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:40.487996image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:41.441443image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:42.417602image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:43.485124image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:30.701828image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:31.685496image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:32.692779image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:33.674358image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:34.627821image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:35.610011image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:36.566860image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:37.534516image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:38.518667image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:39.562282image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:40.555291image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:41.513712image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:42.490724image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:43.558260image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:30.770454image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:31.759999image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:32.765170image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:33.743401image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:34.696940image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:35.680841image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:36.633968image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:37.606645image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:38.588313image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:39.640435image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:40.625129image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:41.582003image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:42.563324image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:43.633980image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:30.841083image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:31.835057image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:32.838295image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:33.815520image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:34.765070image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:35.753932image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:36.704082image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:37.677866image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:38.660344image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:39.717804image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:40.694266image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:41.653144image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:42.635990image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:43.705586image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:30.905744image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:31.903209image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:32.906070image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:33.880983image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:34.828904image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:35.820794image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:36.765370image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:37.742500image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:38.727740image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:39.788914image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:19:33.947751image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:19:40.822254image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:41.781532image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:42.771050image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:43.866227image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:31.042957image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:32.045463image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:33.048637image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:34.020526image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:34.967599image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:35.960918image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:36.899638image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:37.882385image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:38.868459image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:39.942445image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:40.892428image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:41.852166image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:19:42.842619image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Missing values

2024-09-29T20:19:43.952461image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-29T20:19:44.111292image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-29T20:19:44.319171image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

14615_FERM0101.Temperatura_PV14615_FERM0101.Single_Use_pH_PV14615_FERM0101.Single_Use_DO_PV14615_FERM0101.PUMP_2_TOTAL14615_FERM0101.PUMP_2_PV14615_FERM0101.PUMP_1_TOTAL14615_FERM0101.PUMP_1_PV14615_FERM0101.pH_2_PV14615_FERM0101.pH_1_PV14615_FERM0101.Load_Cell_Net_PV14615_FERM0101.Gas_Overlay_PV14615_FERM0101.DO_2_PV14615_FERM0101.DO_1_PV14615_FERM0101.Biocontainer_Pressure_PV14615_FERM0101.Air_Sparge_PV14615_FERM0101.Agitation_PV
DateTime
2023-03-15 00:00:00.00030.2398985.90420.719247550.1865720.022.4399960.0-0.2347635.9141821636.83.9998740.018.7320300.2689690.00000080.0
2023-03-15 00:15:00.00029.7199665.90427.383203550.1865720.022.4399960.0-0.2347635.9220701636.43.9999880.025.4404980.4109100.00000080.0
2023-03-15 00:30:00.00028.8719865.90420.657001550.1865720.022.4399960.0-0.2347635.9226651636.83.9996540.019.0107040.2083310.00000080.0
2023-03-15 00:45:00.00028.8799835.90416.427148550.1865720.022.4399960.0-0.2347635.9220801637.23.9998360.014.2580150.28934925.69748280.0
2023-03-15 01:00:00.00029.4242835.89621.849701550.1865720.022.4399960.0-0.2347635.9220801636.84.0003370.020.1254040.1878400.00000080.0
2023-03-15 01:15:00.00030.0324955.89618.105228550.1865720.022.4399960.0-0.2347635.9141861636.84.0011100.014.9521820.69774335.09388180.0
2023-03-15 01:30:00.00030.2242955.89620.733293550.1865720.022.4399960.0-0.2347635.9059921636.84.0001090.018.8688340.2083310.00000080.0
2023-03-15 01:45:00.00030.0800055.89628.040974550.1865720.022.4399960.0-0.2347635.9059911636.44.0006800.025.7242370.1678220.00000080.0
2023-03-15 02:00:00.00029.8560065.88819.055214550.1865720.022.4399960.00.0000005.9062791636.43.9996760.017.1917160.2482280.00000080.0
2023-03-15 02:15:00.00029.4718305.88825.434689550.1865720.022.4399960.0-0.2347635.9062791636.84.0001600.023.6215090.1680850.00000080.0
14615_FERM0101.Temperatura_PV14615_FERM0101.Single_Use_pH_PV14615_FERM0101.Single_Use_DO_PV14615_FERM0101.PUMP_2_TOTAL14615_FERM0101.PUMP_2_PV14615_FERM0101.PUMP_1_TOTAL14615_FERM0101.PUMP_1_PV14615_FERM0101.pH_2_PV14615_FERM0101.pH_1_PV14615_FERM0101.Load_Cell_Net_PV14615_FERM0101.Gas_Overlay_PV14615_FERM0101.DO_2_PV14615_FERM0101.DO_1_PV14615_FERM0101.Biocontainer_Pressure_PV14615_FERM0101.Air_Sparge_PV14615_FERM0101.Agitation_PV
DateTime
2024-09-10 21:45:00.0006.391998799.791992799.9919920.00.07.920.0-0.2644465.7253021574.43.9993040.00.0-0.1765080.020.0
2024-09-10 22:00:00.0006.149648799.791992799.9919920.00.07.920.0-0.2644465.7253021574.44.0003230.00.0-0.4374130.020.0
2024-09-10 22:15:00.0005.971358799.791992799.9919920.00.07.920.0-0.2644465.7330961574.44.0006200.00.0-0.4600710.020.0
2024-09-10 22:30:00.0005.767999799.791992799.9919920.00.07.920.0-0.2644465.7330961574.44.0005110.00.0-0.5613400.020.0
2024-09-10 22:45:00.0005.608002799.791992799.9919920.00.07.920.0-0.2644465.7330961574.43.9999740.00.0-0.4600710.020.0
2024-09-10 23:00:00.0005.471997799.791992799.9919920.00.07.920.0-0.2644465.7411791574.44.0002110.00.0-0.5005800.020.0
2024-09-10 23:15:00.0005.391998799.791992799.9919920.00.07.920.0-0.2644465.7330961574.44.0000820.00.0-0.4662580.020.0
2024-09-10 23:30:00.0005.359998799.791992799.9919920.00.07.920.0-0.2644465.7253021574.84.0004210.00.0-0.5582420.020.0
2024-09-10 23:45:00.0005.384003799.791992799.9919920.00.07.920.0-0.2644465.7172191574.84.0000590.00.0-0.5330660.020.0
2024-09-11 00:00:00.0005.408002799.791992799.9919920.00.07.920.0-0.2644465.7091361574.83.9998540.00.0-0.4479840.020.0

Duplicate rows

Most frequently occurring

14615_FERM0101.Temperatura_PV14615_FERM0101.Single_Use_pH_PV14615_FERM0101.Single_Use_DO_PV14615_FERM0101.PUMP_2_TOTAL14615_FERM0101.PUMP_2_PV14615_FERM0101.PUMP_1_TOTAL14615_FERM0101.PUMP_1_PV14615_FERM0101.pH_2_PV14615_FERM0101.pH_1_PV14615_FERM0101.Load_Cell_Net_PV14615_FERM0101.Gas_Overlay_PV14615_FERM0101.DO_2_PV14615_FERM0101.DO_1_PV14615_FERM0101.Biocontainer_Pressure_PV14615_FERM0101.Air_Sparge_PV14615_FERM0101.Agitation_PV# duplicates
1103NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4352
65817.671997800.056006695.1012217987.8195310.017.1599980.0-0.2347631.497357-19.20.00.00.0480.00.00.0224
13314.831995799.791992659.7328139120.9796870.040.4799990.0-0.2644461.416254-18.00.00.00.0480.00.00.023
12514.783997799.791992659.7328139120.9796870.040.4799990.0-0.2644461.416254-18.00.00.00.0480.00.00.022
10714.656006799.791992659.7328139120.9796870.040.4799990.0-0.2644461.416254-18.00.00.00.0480.00.00.019
11014.695996799.791992659.7328139120.9796870.040.4799990.0-0.2644461.416254-18.00.00.00.0480.00.00.019
11314.704004799.791992659.7328139120.9796870.040.4799990.0-0.2644461.416254-18.00.00.00.0480.00.00.018
11514.728003799.791992659.7328139120.9796870.040.4799990.0-0.2644461.416254-18.00.00.00.0480.00.00.017
87524.247998799.791992659.7328139120.9796870.040.4799990.01.5643681.300273-18.80.00.00.0480.00.00.017
108226.480005799.791992659.7328139120.9796870.040.4799990.01.5643681.300273-18.80.00.00.0480.00.00.016